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Hydrography90m: pushing the boundaries of computational hydrology

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Hydrography90m: pushing the boundaries of computational hydrology
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7
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production Year2022-2023
Production PlaceWageningen

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Abstract
On November 30th, the Open-Earth-Monitor project led by OpenGeoHub hosted Giuseppe Amatulli, a research scientist at Yale University. Dr Amatulli is a passionate forester and spatial modeller by training (M.Sc. & PhD in GeoScience and Forestry) and a computer scientist by trade. His research activity is mainly dedicated to spatial modelling, GIS and remote sensing with particular emphasis on the species distribution model, areal distribution and potential shift under climate change conditions, wildland fire occurrence and pattern recognition, and wildfire risk assessment based on human and bio-physical parameters. Streams and rivers drive several processes in hydrology, geomorphology, geography and ecology. A global starized hydrographic network that accurately delineates streams and rivers and their topographic and topological properties is needed for worldwide environmental applications. Using the MERIT Hydro Digital Elevation Model at 90m and by employing a suite of GRASS GIS hydrological modules, we calculated the range-wide upstream flow accumulation and flow direction to delineate a total of 1.6 million drainage basins and extracted globally a total of 726 million unique stream segments with their corresponding sub-catchments. Besides, we computed stream topographic variables comprising stream slope, gradient, length, and curvature attributes as well as stream topological variables to allow for network routing and various stream order classifications. The validation shows that the newly developed Hydrography90m has the highest spatial precision and contains more headwater stream channels compared to three other global hydrographic datasets. More information at https://doi.org/10.5194/essd-14-4525-2022 & https://hydrography.org/
Keywords
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Artificial neural networkDiscrete element methodLibrary (computing)HydrographImage resolutionStreaming mediaAreaGradientGrass (card game)DigitizingPatch (Unix)AreaStreaming mediaLimit (category theory)Parallel portInformationArtificial neural networkMultiplication signShift operatorPixelPhysical systemDifferential equationFlow separationTesselationEndliche ModelltheorieMetreComputational intelligenceBefehlsprozessorLocal ringSupercomputerGrass (card game)Product (business)outputEntire functionWater vaporUniform resource locatorSatelliteError messageDegree (graph theory)Image resolutionMatrix (mathematics)Metric systemAssembly languageImplementationLevel (video gaming)ForestFormal verificationBifurcation theoryAlgorithmTerm (mathematics)Machine visionPoint (geometry)Heegaard splittingQuicksortCommunications protocolWordMedical imagingOpen setProcess (computing)Web pageComputer animation
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Transcript: English(auto-generated)